Aktürk, Berkay

Profile URL
https://hdl.handle.net/20.500.14627/1225
Name Variants
Job Title
Arş. Gör. Uzm.
Email Address
berkay.akturk@fbu.edu.tr
Main Affiliation
Veri Bilimi ve Analitiği Bölümü
Status
Current Staff
Website
ORCID ID
0000-0002-7829-6409
Scopus Author ID
58631712200
Turkish CoHE Profile ID
Google Scholar ID
ui6lTZ0AAAAJ
WoS Researcher ID
NVM-4695-2025

Research Topics

Domains
Physical SciencesHealth Sciences
Fields
MathematicsNursingMedicine
Subfields
Statistics and ProbabilityNutrition and DieteticsEpidemiology
Specific Research Areas
Statistical Methods and Inference
Advanced Statistical Methods and Models
Statistical Methods and Bayesian Inference
Child Nutrition and Water Access
Breastfeeding Practices and Influences

Sustainable Development Goals

SDG data is not available

Publication Collaboration

Affiliation Name Count
Marmara University 2
Sakarya University 1
Universidad Autónoma de Ciudad Juárez 1
Sultan Idris Education University 1
Tribhuvan University 1
1 / 4
Data obtained from OpenAlex
Scholarly Output

1

Articles

0

WoS Citation Count

0

Scopus Citation Count

0

Author of Publication: search.filters.isAuthorOfPublication.e7755ce5-925e-47e2-a267-fc298689d2a0

Scholarly Output Search Results

Now showing 1 - 1 of 1
  • Conference Object
    Classification of Noisy Histopathology Images with Bayesian and Ensemble Deep Learning
    (SPIE, 2025) Akturk, B.; Bilgin, G.
    The classification of histopathology image data using deep learning algorithms is a pivotal aspect in cancer diagnosis, offering crucial benefits in terms of accuracy and rapid decision-making for pathologists, technologists, clinicians, and researchers. However, the complex process of data structuring and labeling often involves the presence of noisy or outlier labels and images, which inevitably diminish the training and predictive efficacy of classification models. This study introduces an innovative approach for classifying histopathology images with noisy characteristics, presenting a learning process resilient to noise. In this dataset, where classification performance is inherently challenged, popular transfer learning models are integrated with Bayesian deep learning and ensemble learning algorithms. This integration helps mitigate common problems such as overfitting and underfitting in outlier data predictions, yielding more stable outcomes. These hybrid methods, unaffected by the noise and uncertainty in data structures, demonstrate performance comparable to, or even surpassing, transfer learning algorithms. Consequently, the viability of addressing real-world noisy dataset scenarios is enhanced. Experimental results indicate that the proposed ensemble Bayesian transfer learning algorithms match the success of pre-trained transfer learning algorithms and offer an alternative to time-consuming model validation techniques, underscoring the practical application of ensemble Bayesian transfer learning in enhancing uncertainty assessments. · © 2025 SPIE · 0277-786X ·